Infeasible interior-point algorithms based on sampling average approximations for a class of stochastic complementarity problems and their applications
type: Item
instance of: scholarly article
title: Infeasible interior-point algorithms based on sampling average approximations for a class of stochastic complementarity problems and their applications
zbMATH Open document ID: 1410.90143
author: Zhen-Ping Yang
author: Jin Zhang
author: Guihua Lin
author: Xi-De Zhu
published in: Journal of Computational and Applied Mathematics
publication date: 2019-03-26T00:00:00Z
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C33
Mathematics Subject Classification ID: 90C51
zbMATH DE Number: 7042033
zbMATH Keywords: nonlinear programming
zbMATH Keywords: stochastic complementarity problem
zbMATH Keywords: sample average approximation
zbMATH Keywords: infeasible interior-point algorithm
zbMATH Keywords: natural gas transmission and oligopoly models
MaRDI profile type: Publication
full work available at URL: j.cam.2018.12.013
OpenAlex ID: W2904629689
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Wikidata QID: Q128709612
DOI: 10.1016/J.CAM.2018.12.013
description: scientific article; zbMATH DE number 7042033